Are Vascular Risk Factors Associated With Post-Stroke Depressive Symptoms?
Bibliographic record
Abstract
OBJECTIVE: Vascular risk factors (VRFs) have been associated with stroke and cognitive impairment, however, the role of VRFs in predicting post-stroke depression (PSD) has not been assessed. The objective of the current study was to determine whether VRFs are associated with the risk of PSD in an acute stroke population. METHODS: In this observational study, patients meeting World Health Organization MONICA Project and National Institute of Neurological Disorders and Stroke criteria for stroke were eligible. Patients were assessed for depression, cognition, and stroke severity, and VRF and demographic information were obtained. RESULTS: A total of 102 patients were recruited within 4 months post-stroke. Using a score of ≥16 on the Center for Epidemiological Studies Depression scale to determine depressive symptoms, 38 patients (age 72.1 ± 15.6, 44.7% male) screened positive for depressive symptoms and 64 (age 70.1 ± 13.6, 51.6% male) screened negative. Analysis of VRFs showed that only hypertension (P = .044) independently predicted the presence of depressive symptoms (χ(2) = 4.742, P = .029, Nagelkerke R (2) = .062). CONCLUSIONS: Hypertension was associated with post-stroke depressive symptoms, while there was no relationship between PSD and other VRFs. Hypertension may have a greater impact than other VRFs on mood following stroke and may have a role in prevention and treatment of PSD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".